Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

Recent research shows that collaboration and social networking foster knowledge sharing and innovation by sparking new connections, ideas, and practices. Yet these informal networks are often misunderstood and poorly managed. Building on the groundbreaking, bestselling first edition, Knowledge Management Handbook: Collaboration and Social Networkin

This book constitutes the refereed proceedings of Satellite Events held at the 19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014 in November 2014. EKAW 2014 hosted three satellite workshops: VISUAL 2014, International Workshop on Visualizations and User Interfaces for Knowledge Engineering and Linked Data Analytics, EKM1, the First International Workshop on Educational Knowledge Management and ARCOE-Logic 2014, the 6th International Workshop on Acquisition, Representation and Reasoning about Context with Logic. This volume also contains the accepted contributions for the EKAW 2014 tutorials, demo and poster sessions.

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

Society is now facing challenges for which the traditional management toolbox is increasingly inadequate. Well-grounded theoretical frameworks, such as systems thinking and cybernetics, offer general level interpretation schemes and models that are capable of supporting understanding of complex phenomena and are not impacted by the passage of time. This book serves the knowledge society to address the complexity of decision making and problem solving in the 21st century with contributions from systems and cybernetics. A multi-disciplinary approach has been adopted to support diversity and to develop inter- and trans-disciplinary knowledge within the shared thematic of problem solving and decision making in the 21st century. Its conceptual thread is cyber/systemic thinking, and its realisation is supported by a wide network of scientists on the basis of a highly participative agenda. The book provides a platform of knowledge sharing and conceptual frameworks developed with multi-disciplinary perspectives, which are useful to better understand the fast changing scenario and the complexity of problem solving in the present time.

Knowledge Management has evolved into one of the most important streams of management research, affecting organizations of all types at many different levels. The Encyclopedia of Knowledge Management, Second Edition provides a compendium of terms, definitions and explanations of concepts, processes and acronyms addressing the challenges of knowledge management. This two-volume collection covers all aspects of this critical discipline, which range from knowledge identification and representation, to the impact of Knowledge Management Systems on organizational culture, to the significant integration and cost issues being faced by Human Resources, MIS/IT, and production departments.

The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.

For the last two decades, IS researchers have conducted empirical studies leading to better understanding of the impact of Systems Analysis and Design methods in business, managerial, and cultural contexts. SA & D research has established a balanced focus not only on technical issues, but also on organizational and social issues in the information society.This volume presents the very latest, state-of-the-art research by well-known figures in the field. The chapters are grouped into three categories: techniques, methodologies, and approaches.